Personalized news recommendation: Methods and challenges
Personalized news recommendation is important for users to find interesting news
information and alleviate information overload. Although it has been extensively studied …
information and alleviate information overload. Although it has been extensively studied …
Melu: Meta-learned user preference estimator for cold-start recommendation
This paper proposes a recommender system to alleviate the cold-start problem that can
estimate user preferences based on only a small number of items. To identify a user's …
estimate user preferences based on only a small number of items. To identify a user's …
[HTML][HTML] The online misinformation engagement framework
Research on online misinformation has evolved rapidly, but organizing its results and
identifying open research questions is difficult without a systematic approach. We present …
identifying open research questions is difficult without a systematic approach. We present …
Fairness in recommendation ranking through pairwise comparisons
A Beutel, J Chen, T Doshi, H Qian, L Wei… - Proceedings of the 25th …, 2019 - dl.acm.org
Recommender systems are one of the most pervasive applications of machine learning in
industry, with many services using them to match users to products or information. As such it …
industry, with many services using them to match users to products or information. As such it …
Denoising implicit feedback for recommendation
The ubiquity of implicit feedback makes them the default choice to build online
recommender systems. While the large volume of implicit feedback alleviates the data …
recommender systems. While the large volume of implicit feedback alleviates the data …
Deep neural networks for youtube recommendations
P Covington, J Adams, E Sargin - … of the 10th ACM conference on …, 2016 - dl.acm.org
YouTube represents one of the largest scale and most sophisticated industrial
recommendation systems in existence. In this paper, we describe the system at a high level …
recommendation systems in existence. In this paper, we describe the system at a high level …
Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
Building a successful recommender system depends on understanding both the dimensions
of people's preferences as well as their dynamics. In certain domains, such as fashion …
of people's preferences as well as their dynamics. In certain domains, such as fashion …
Towards a fair marketplace: Counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems
Two-sided marketplaces are platforms that have customers not only on the demand side (eg
users), but also on the supply side (eg retailer, artists). While traditional recommender …
users), but also on the supply side (eg retailer, artists). While traditional recommender …
VBPR: visual bayesian personalized ranking from implicit feedback
Modern recommender systems model people and items by discovering orteasing apart'the
underlying dimensions that encode the properties of items and users' preferences toward …
underlying dimensions that encode the properties of items and users' preferences toward …
Reinforcement learning to optimize long-term user engagement in recommender systems
Recommender systems play a crucial role in our daily lives. Feed streaming mechanism has
been widely used in the recommender system, especially on the mobile Apps. The feed …
been widely used in the recommender system, especially on the mobile Apps. The feed …